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第 1 期 赵亚宁等: 基于计算机视觉的大黄鱼个体身份识别 117
ion using canonical discriminant analysis[J]. ICES 21.
Journal of Marine Science, 1995, 52(1): 145 − 149. [17] Huang P X, Boom B J, Fisher R B. Hierarchical classi-
[11] Strachan N J C, Nesvadba P, Allen A R. Fish species re- fication with reject option for live fish recognition[J].
cognition by shape analysis of images[J]. Pattern Re- Machine Vision and Applications, 2015, 26(1): 89 − 102.
cognition, 1990, 23(5): 539 − 544. [18] Hook L, Bradshaw T. Google parent Alphabet invents
[12] Ruff B P, Marchant J A, Frost A R. Fish sizing and mon- fish recognition system[EB/OL]. (2020-03-02)[2022-
itoring using a stereo image analysis system applied to 10-09]. https://caaquaculture.org/2020/03/04/google-par-
fish farming[J]. Aquacultural Engineering, 1995, 14(2): ent-alphabet-invents-fish-recognition-system/.
155 − 173. [19] Dala-Corte R B, Moschetta J B, Becker F G. Photo-iden-
[13] Harvey E, Shortis M. A system for stereo-video meas- tification as a technique for recognition of individual
urement of sub-tidal organisms[J]. Marine Technology fish: a test with the freshwater armored catfish Rinelori-
Society Journal, 1995, 29(4): 10 − 22. caria aequalicuspis Reis & Cardoso, 2001 (Siluriformes:
[14] Rova A, Mori G, Dill L M. One fish, two fish, butterfish, Loricariidae)[J]. Neotropical Ichthyology, 2016, 14(1):
trumpeter: recognizing fish in underwater video[C]// e150074.
MVA. Proceedings of the IAPR Conference on Machine [20] Al-Jubouri Q, Al-Azawi R J, Al-Taee M, et al. Efficient
Vision Applications. Tokyo: MVA, 2007: 404 − 407. individual identification of zebrafish using Hue/Satura-
[15] Spampinato C, Giordano D, Di Salvo R, et al. Automat- tion/Value color model[J]. Egyptian Journal of Aquatic
ic fish classification for underwater species behavior un- Research, 2018, 44(4): 271 − 277.
derstanding[C]//ACM. Proceedings of the First ACM [21] Cisar P, Bekkozhayeva D, Movchan O, et al. Computer
International Workshop on Analysis and Retrieval of vision based individual fish identification using skin dot
Tracked Events and Motion in Imagery Streams. Firenze: pattern[J]. Scientific Reports, 2021, 11(1): 16904.
ACM, 2010: 45 − 50. [22] He K M, Zhang X Y, Ren S Q, et al. Deep residual learn-
[16] Hsiao Y H, Chen C C, Lin S I, et al. Real-world under- ing for image recognition[C]//IEEE. Proceedings of the
water fish recognition and identification, using sparse IEEE Conference on Computer Vision and Pattern Re-
representation[J]. Ecological Informatics, 2014, 23: 13 − cognition. Las Vegas: IEEE, 2016: 770 − 778.

